Experimental Validation of Approximate Dynamic Programming Based optimization and Convergence on Microgrid Applications
Experimental Validation of Approximate Dynamic Programming Based optimization and Convergence on Microgrid Applications
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DOI：
10.1109/pesgm41954.2020.9281629
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
Avijit Das;Z. Ni;Xiangnan Zhong;Di Wu
中科院分区：
文献类型：
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作者：
Avijit Das;Z. Ni;Xiangnan Zhong;Di Wu
随机优化可以更好地模拟电力系统问题中的不确定性。然而，当状态空间和动作空间变得很大时，许多现有的方法变得计算昂贵，甚至不可行来解决问题。近似动态规划（ADP）作为求解电力系统优化问题的一种有效工具，以其较低的计算代价而受到研究者的关注。在本文中，根据现有的文献中，我们研究了ADP方法与后决策值函数近似收敛到近最优解，提高计算速度和实验验证的性能的方法的微电网能量优化问题。研究了后判决ADP算法的收敛性，分析了逼近误差与迭代次数的关系。提供了一个流程图来说明所提出的ADP算法的微电网能量优化问题。ADP和动态规划（DP）的性能进行了比较的优化误差和计算时间。结果表明，与传统的DP方法相比，后决策ADP方法可以实现具有竞争力的最优性，并提高了计算速度。
Stochastic optimization can better model uncertainties in power system problems. However, when state space and action space become large, many existing approaches become computationally expensive and even infeasible to solve the problem. Approximate dynamic programming (ADP) attracts researchers’ attention as a powerful tool for solving power system optimization problems with reduced computational cost. In this paper, in light of the existing literature, we investigate how the ADP approach with post-decision value function approximation converges to the nearly optimal solution with improved computational speed and experimentally validate the performance of the approach for a microgrid energy optimization problem. The approximation error versus the number of iteration is studied for convergence analysis of the post-decision ADP. A flowchart is provided to illustrate the proposed ADP algorithm for a microgrid energy optimization problem. The performance of ADP and dynamic programming (DP) is compared in terms of optimization error and computational time. It has found that the post-decision ADP approach can achieve competitive optimality with improved computational speed compared to the traditional DP.